Differential impact of brain network efficiency on post-stroke motor and attentional deficits
Evangelista, G. G.; Egger, P.; Bruegger, J.; Beanato, E.; Koch, P. J.; Ceroni, M.; Fleury, L.; Cadic-Melchior, A.; Meyer, N. H.; de Leon Rodriguez, D.; Girard, G.; Leger, B.; Turlan, J.-L.; Muehl, A.; Vuadens, P.; Adolphsen, J.; Jagella, C.; Constantin, C.; Alvarez, V.; Ghika, J.-A.; San Millan, D.; Bonvin, C.; Morishita, T.; Wessel, M. J.; Van de Ville, D.; Hummel, F.
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BackgroundMost studies on stroke have been designed to examine one deficit in isolation, yet survivors often have multiple deficits in different domains. While the mechanisms underlying multiple-domain deficits remain poorly understood, network-theoretical methods may open new avenues of understanding. Methods50 subacute stroke patients (7{+/-}3days post-stroke) underwent diffusion-weighted magnetic resonance imaging and a battery of clinical tests of motor and cognitive functions. We defined indices of impairment in strength, dexterity, and attention. We also computed imaging-based probabilistic tractography and whole brain connectomes. Overlaying individual lesion masks onto the tractograms enabled us to split the connectomes into their affected and unaffected parts and associate them to impairment. ResultsTo efficiently integrate inputs from different sources, brain networks rely on a "rich-club" of a few hub nodes. Lesions harm efficiency, particularly when they target the rich-club. We computed efficiency of the unaffected connectome, and found it was more strongly correlated to impairment in strength, dexterity and attention than efficiency of the total connectome. The magnitude of the correlation between efficiency and impairment followed the order attention > dexterity {approx} strength. Network weights associated with the rich-club were more strongly correlated to efficiency than non-rich-club weights. ConclusionsAttentional impairment is more sensitive to disruption of coordinated network activity between brain regions than motor impairment, which is sensitive to disruption of localized network activity. Providing more accurate reflections of actually functioning parts of the network enables the incorporation of information about the impact of brain lesions on connectomics contributing to a better understanding of underlying stroke mechanisms.
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